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Abstract:
The current revolution in sequencing technologies allows us to obtain a much more detailed picture of transcriptomes. Studying them under different conditions or in mutants will lead to a considerably improved understanding of the underlying mechanisms of gene expression and processing. An important prerequisite is to be able to accurately determine the full complement of RNA transcripts and to infer their abundance in the cell. We present the first integrative platform for quantitatively analyzing RNA- seq experiments. It is based on the Galaxy-framework [1] and builds on recently developed methods for NGS sequence analysis: a) We extended the alignment method QPALMA [2] and combined it with GenomeMapper [3] to align both spliced and unspliced reads with high accuracy, while taking advantage read quality information and splice site predictions. b) We extended the gene finding system mGene [4] to take advantage of read alignments to more accurately predict gene structures de novo. c) We developed the method rQuant that simultaneously estimates biases inherent in sequencing protocols and determines the abundances of transcripts [5]. It more accurately predicts abundances of alternative transcripts. d) Finally, we developed test techniques that determine significant differences between two RNA-seq experiments to find differentially expressed regions (with or without knowledge of transcripts). The platform can be used for many purposes, including 1) to (re-)annotate genomes while profiting from NGS data; 2) to identify novel transcripts that are only expressed under certain conditions; and 3) to identify regions or transcripts that are the target of gene or RNA processing regulation. We have tested the system with data from several model organisms and human. Moreover, we have participated in the RGASP competition for an external evaluation of alignment, transcript identification and quantification accuracy.
